Fast Calibrated Explanations: Efficient and Uncertainty-Aware Explanations for Machine Learning Models

Abstract

This paper introduces Fast Calibrated Explanations, an extension of an existing explanation method, Calibrated Explanations, designed for generating rapid, uncertainty-aware explanations for machine learning models. By incorporating perturbation techniques from ConformaSight, a global explanation method, into the core elements of Calibrated Explanations, we achieved significant speedups. These core elements include local feature importance with calibrated predictions, both of which retain uncertainty quantification. While the extension sacrifices some degree of detail, it excels in computational efficiency, making it ideal for high-stakes, real-time applications. Fast Calibrated Explanations applies to probabilistic explanations in classification and thresholded regression tasks, providing the probability of a target being above or below a user-defined threshold. This approach maintains the versatility of Calibrated Explanations for both classification and thresholded regression, making it suitable for a range of predictive tasks where uncertainty quantification is crucial.